A-WP3 – PastLand II

Summary and structure:

Predictions of the global climate depend on both the model's initial state and the anticipated change in aerosols and greenhouse gases; for decadal predictions anthropogenic climate change and natural variability are expected to be equally important. A close representation of the observed climate state in global coupled climate models is therefore crucial for (the initialization of) decadal predictions. However, predictability beyond two weeks is essentially influenced by time scales which are longer than typical scales of weather phenomena. These slow components which affect seasonal to decadal predictability of the Earth system are beside the oceans, glaciers and sea ice, the moisture content of the soil, snow cover and the terrestrial biosphere.

During its first phase, Pastland was active in land surface model development to allow for a more realistic representation of soil moisture memory, the evaluation of state of the art soil moisture observations as well as conducting experiments to identify regions and time scales of land surface memory. Thus, the necessary prerequisites were fulfilled to allow for the assimilation of land surface observations into the Earth System Model of the Max Planck Institute for Meteorology (MPI-ESM).This objective is the major task during the second phase of Pastland. An assimilation scheme which is already used in the ocean component of MPI-ESM will be adapted for the land surface and modified to assimilate several land surface observational streams. The best suitable land surface variables will be identified by extending the evaluation analysis done in Pastland1 for further variables like surface or soil temperature and snow cover. The added value of land surface initialization can then be analyzed in hindcast ensemble simulations with the MPI-ESM. Finally, Pastland2 will evaluate whether or not there are improvements in the model's predictive skill that justify the effort of the additional assimilation.

Objectives:

Identify land surface variables beside soil moisture that contribute to overall land surface memory and predictability in simulation and observation

Evaluate state of the art land surface observations in respect to their potential suitability for land surface assimilation

Adapting the MiKlip assimilation and initialization routines for the assimilation of several land surface data streams

Evaluate the prediction skill for the MiKlip Prediction System with and without land surface assimilation

Deliverables:

Database of land surface observations suitable for assimilation

Land surface assimilation and initialization scheme

Decadal hindcast simulations

Progress so far

Recently, the prototype version of the land surface assimilation scheme was developed and implemented into JSBACH, which is the land surface component of the MPI-ESM. From a technical point of view, a major requirement for the scheme was a self-contained structure with as few interactions with the model as possible. This facilitates an easy and fast implementation into the MiKlip Prediction System later on. Furthermore, the developed module structure allows for a straightforward extension with respect to other land surface variables.

The scheme employs a nudging algorithm following Brocca et al (2010), where top layer soil moisture is updated once per day. This is consistent with the temporal resolution of available observations, while preserving the sub-daily variability generated by the model.

Preliminary results of soil moisture assimilation simulations indicate that the effect of assimilation is strongest on the land surface, e.g. altering the South Asian monsoon index significantly. Over the ocean, its impact is limited to smaller regions and affects the salinity around the outlets of large rivers.